Advancements in Artificial Intelligence for Medical Images

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: 28 February 2025 | Viewed by 963

Special Issue Editor


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Guest Editor
Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London E1 4NS, UK
Interests: AI for precision dentistry and medicine; image processing; time series analysis; pattern recognition; bioinformatics; chaos and nonlinear dynamics

Special Issue Information

Dear Colleagues,

We are pleased to announce a call for papers for a Special Issue of Journal of Imaging, dedicated to “Advancements in Artificial Intelligence for Medical Images”. This Special Issue aims to explore cutting-edge research and innovative applications of artificial intelligence (AI) in the field of medical imaging.

Scope and Topics:

The integration of AI into medical imaging is revolutionizing healthcare, offering unprecedented improvements in diagnoses, treatment planning, and patient outcomes. This Special Issue invites original research articles, comprehensive reviews, and insightful case studies that focus on, but are not limited to, the following topics:

  • Deep Learning for Medical Imaging: Novel architectures, training techniques, and applications of deep learning models in image analysis, segmentation, and classification.
  • Imaging Biomarkers: AI-driven extraction and the analysis of quantitative features from medical images for personalized medicine.
  • Computer-Aided Diagnosis (CAD): The development and validation of CAD systems that assist clinicians in interpreting medical images.
  • Image Reconstruction and Enhancement: AI techniques for improving image quality, resolution, and reducing artifacts in medical imaging modalities.
  • Multimodal Imaging and Data Integration: AI methods for combining information from different imaging modalities (e.g., MRI, CT, PET) and integrating with other clinical data.
  • Ethics and Explainability in AI: Addressing ethical considerations, bias, and the development of interpretable AI models for medical imaging.
  • AI in Screening and Early Detection: AI applications in the early detection and screening of diseases using medical images.
  • Real-time and edge AI (AI on the edge) Applications: Implementing AI for real-time image analysis and decision support in clinical settings, including edge computing solutions.
  • Regulatory and Clinical Adoption: Challenges and strategies for the regulatory approval, clinical validation, and adoption of AI tools in healthcare systems.

Prof. Dr. Tuan D. Pham
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • medical imaging
  • deep learning
  • image analysis

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Published Papers (1 paper)

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Research

20 pages, 4678 KiB  
Article
Deep Learning-Based Diagnosis Algorithm for Alzheimer’s Disease
by Zhenhao Jin, Junjie Gong, Minghui Deng, Piaoyi Zheng and Guiping Li
J. Imaging 2024, 10(12), 333; https://doi.org/10.3390/jimaging10120333 - 23 Dec 2024
Viewed by 602
Abstract
Alzheimer’s disease (AD), a degenerative condition affecting the central nervous system, has witnessed a notable rise in prevalence along with the increasing aging population. In recent years, the integration of cutting-edge medical imaging technologies with forefront theories in artificial intelligence has dramatically enhanced [...] Read more.
Alzheimer’s disease (AD), a degenerative condition affecting the central nervous system, has witnessed a notable rise in prevalence along with the increasing aging population. In recent years, the integration of cutting-edge medical imaging technologies with forefront theories in artificial intelligence has dramatically enhanced the efficiency of identifying and diagnosing brain diseases such as AD. This paper presents an innovative two-stage automatic auxiliary diagnosis algorithm for AD, based on an improved 3D DenseNet segmentation model and an improved MobileNetV3 classification model applied to brain MR images. In the segmentation network, the backbone network was simplified, the activation function and loss function were replaced, and the 3D GAM attention mechanism was introduced. In the classification network, firstly, the CA attention mechanism was added to enhance the model’s ability to capture positional information of disease features; secondly, dilated convolutions were introduced to extract richer features from the input feature maps; and finally, the fully connected layer of MobileNetV3 was modified and the idea of transfer learning was adopted to improve the model’s feature extraction capability. The results of the study showed that the proposed approach achieved classification accuracies of 97.85% for AD/NC, 95.31% for MCI/NC, 93.96% for AD/MCI, and 92.63% for AD/MCI/NC, respectively, which were 3.1, 2.8, 2.6, and 2.8 percentage points higher than before the improvement. Comparative and ablation experiments have validated the proposed classification performance of this method, demonstrating its capability to facilitate an accurate and efficient automated auxiliary diagnosis of AD, offering a deep learning-based solution for it. Full article
(This article belongs to the Special Issue Advancements in Artificial Intelligence for Medical Images)
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